Real Data Augmentation for Medical Image Classification

Citation data:

Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, ISSN: 0302-9743, Vol: 10552 LNCS, Page: 67-76

Publication Year:
2017

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DOI:
10.1007/978-3-319-67534-3_8
Author(s):
Chuanhai Zhang, Wallapak Tavanapong, Johnny Wong, Piet C. de Groen, Jung Hwan Oh
Publisher(s):
Springer Nature
Tags:
Mathematics, Computer Science
book chapter description
Many medical image classification tasks share a common unbalanced data problem. That is images of the target classes, e.g., certain types of diseases, only appear in a very small portion of the entire dataset. Nowadays, large collections of medical images are readily available. However, it is costly and may not even be feasible for medical experts to manually comb through a huge unlabeled dataset to obtain enough representative examples of the rare classes. In this paper, we propose a new method called Unified LF&SM to recommend most similar images for each class from a large unlabeled dataset for verification by medical experts and inclusion in the seed labeled dataset. Our real data augmentation significantly reduces expensive manual labeling time. In our experiments, Unified LF&SM performed best, selecting a high percentage of relevant images in its recommendation and achieving the best classification accuracy. It is easily extendable to other medical image classification problems.

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